We first present overall results on the synthetic Synthia segmentation dataset [1]} using DeepLab-ResNet18 and -ResNet 101 in Fig. REF . On both plots, the x-axis represents mean IoU and y-axis is mean accuracy. Weighted CE and Balanced CE improve the mean accuracy considerably compared to the baseline Cross-Entropy loss. However, the improvement comes at a cost of lower mean IoU. Focal loss performs similarly to the standard cross entropy Cross-Entropy loss without noticeable improvement [2]}, [3]}. We will analyze this behavior in Sec. REF . OHEM performs worse on both metrics. We think this is because OHEM completely discards \(30\%\)  training samples at each iteration and this negatively affects feature learning. SoftIoU performs reasonably well and improves both IoU and accuracy compared to the baseline. Nevertheless, the recall loss achieves the highest mean recall besides the weighted CE and Balanced CE with competitive IoU performance. This validates our analysis that the recall loss can balance between precision and recall.
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